Lowest logical fares in Corporate travel
Many corporates use ‘Lowest Logical Fare’ (LLF) for their travel management. But, LLF has a very subjective interpretation. Different stakeholders in the travel ecosystem interpret LLF in different ways.
In my previous company, I had the opportunity to travel from Bangalore to Sydney. The travel desk offered me a Singapore Airline flight that had a stopover in Singapore. This route had a 14:30 hour stop in Singapore and cost around 38,130 INR. I did some research on the net and found some other alternatives that I liked. This was also Singapore Airline Flight, but the stop duration in Singapore was only 1:40 hours. Price for this option was 40,545 INR, around 2,000 INR more. I then advised the travel desk about this alternate route that I had found. The travel agency re-booked me into the flights that I suggested as it was within the budget. When asked, the agency mentioned that they were looking for the best fares. Once could consider either of the options as LLF, under different circumstances. It highlights the limitations of the manual interpretations for even simple cases.
This company mentioned above did not have a very clear definition of LLF. Different corporates have different levels of maturity in the travel policy. Many corporates do have established and mature travel policy. They will often use the term “lowest logical fares” as a part of their policy that the employees need to adhere to. The definition often includes subjective terms like ‘reasonable’, ‘as much as possible’, etc. But even where the LLF is well defined, there is subjectivity in its interpretation. LLF definition is kept simple so that agents can interpret them easily.
Another example of LLF defined by a corporate is as follows:
“Lowest logical fare routing is defined as lowest available Coach Class travel with reasonable connections and total travel time”
Let’s consider a corporate trip requested for 19th Feb 2019 from Los Angeles to New York City. Direct Flight is available at $ 311 while flight with one stop is available for $ 196. In this case, for a domestic flight, one may consider direct flight as reasonable, if available. By that logic, one will choose the direct flight at $311. If a corporate thinks one stop is reasonable then would choose the flight with the fare of $196. In a typical booking tool, the user could sort the results by price and filter it by stops, airline and arrival/departure times. He would then use manual analysis to arrive at the best result. In this particular case, we see two gaps. a) the policy definition itself dependent on interpretation. And b) the existing tools provides limited help in decision making.
Let’s take one more example of the lowest logical fare definition:
The definition of “lowest logical airfare” is the least expensive, non-refundable fare with the minimum number of stops at the time of booking.
In this case, the duration is not considered at all. This may be fine for some scenarios like US domestic travel. But for the international trips where the duration of flights may differ, it leaves room for ambiguity
If the Agents are to follow the LLF then they will have to do their own judgment on which of the parameters is of higher importance. E.g. For Senior management flight duration and the number of stops will be of importance. For junior employees Lower costs will be the main criteria. But a human being will have to be check. That brings in subjectivity into the decision.
Let us consider any currently available self-booking tool in this context. Most of these tools will have filters that would let you filter the offerings based on duration, costs, airline, number of stops, etc.
The picture below shows a typical travel website search result:
The tool does not offer a ranking based on the combined effect of these parameters. It also does not provide any means to change the priority based on the user preferences.
Some other sites like Skyscanner and Ixigo do provide something like a ‘best’ offer. According to them, it is a combination of Fare, Duration and some convenience factors.
But, these too lack a few important aspects. One is the end users preference like Airline, onboard amenities, etc… For example, if the user prefers Singapore airlines, then Singapore airline should come up on the top even if any other airline is marginally cheaper, considering other parameters are the same. The other is the possibility that different groups may have different priorities.
If we are to analyze it, the cost has to be the lowest. But, a bit higher price is acceptable if the duration of the flight is less. But the balance between the cost and duration is a subjective decision. And the case is the same as the number of stops and preferred airline. The current systems do not automate these factors while showing the results. The user will have to filter by Airline, Price, Duration, and Stops and apply his mind and decide. If there is more than one parameter, subjectivity creeps in. If there are more than two parameters it becomes almost impossible to make a judgment
This is where the redefinition of LLF, appropriate for an automated system comes in. For the LLF we need to define the parameters that need to be considered while making the judgment. The primary parameters could be Fare, Duration of travel, Airline and number of stops. There also needs to be an order of priority or weightage for each of these parameters. This will help the system to consider each of the parameters, its priority, and give a comprehensive rating.
But we may have to consider various other aspects to make it holistic.
1. Wifi access: For business travel, the availability of Wifi may be important. As it improves the productivity of the employee.
2. Ancillary fees: extra fee changed for onboard meals, free seat, and allocation. This may add to the total cost while the fare may seem low.
3. Features like Early boarding and advance seat assignment may be the convenience factors that need to be considered.
Handling of complex scenarios is only possible if the routes are ranked automatically. This will need comprehensive LLF definition and their priorities. This could address the problem of subjectivity in the interpretation of LLF.
Let’s take the same example of Bangalore to Sydney. The difference in duration between the two routes is 12:20 Hours and difference in Fare is 2,000 INR. If the difference in duration 12:20 hours has more impact than the price of 2000 INR then the route with a lesser duration will show up higher. There can be another extreme case, where price is the sole decision maker. In that case, the LLF settings can be such that the effect of 2000 INR is far higher than the duration difference of 12:20 hours. Hence the route with lower fare will show up higher. In all cases, the system should do the initial calculation based on the entire gamut of parameters. Handling this without automation would be impossible
A corporate booking tool needs to incorporate these ‘parameters’ that constitute LLF. It should result in automation in the ranking of offers. The corporate should be allowed to changes these parameters for different groups. Artificial intelligence and Machine Learning could further enhance the results where priorities fail to achieve practical results.
